Data base of A bibliometric analysis of the effects of aerobic exercise on overweight (1978–2025)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Overweight is a global public health concern associated with multiple chronic conditions. Aerobic exercise, widely recognized for improving weight control, cardiorespiratory fitness, and metabolic function, has become central to intervention strategies. However, the global research landscape addressing aerobic exercise specifically for overweight populations remains insufficiently mapped. This study aimed to analyze scientific development, main research topics, and collaboration patterns using bibliometric methods based on publications indexed in the Web of Science Core Collection (SCIE and SSCI) from 1978 to 2025 (search date: March 23, 2025). A total of 3,983 documents were retrieved and analyzed using the Bibliometrix package (R), with results visualized through publication trends, co-authorship and co-citation networks, and keyword clustering. The field has expanded rapidly since 2000, showing an average annual growth rate of 7.35%. The United States ranks first in publication volume and citation impact, followed by the United Kingdom and Canada. China demonstrates substantial research output but lower citation performance, highlighting the need for stronger international collaboration. High-impact publishing venues are primarily from exercise science and obesity-related research areas. Keyword and network analyses reveal a shift from general intervention studies toward more specific mechanisms, populations, and multidimensional outcomes, including insulin resistance, body composition, and cardiorespiratory fitness. A strong international collaboration structure, especially within North America and Europe, is also evident. Overall, research on aerobic exercise for overweight individuals has evolved into a multidisciplinary and globally collaborative field. Future efforts should emphasize research quality, deeper collaboration, and tailored intervention strategies to support global health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.031 | 0.152 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it